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Staff MLOps Engineer

1 day ago
United States
Full-time
Lead
Machine Learning Engineer
Software Development
AWS Azure C++ CI/CD Docker Embedded Systems GCP Git Go Kubernetes MLflow MLOps Python Reinforcement Learning Rust
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Apptronik

Apptronik

Apptronik develops versatile humanoid robots to tackle tasks humans prefer not to do, aiming to reshape existence and enhance universal quality of life.

Aerospace & Defense
51-250
Founded 2015
$23M raised
View All Jobs 68

Description

  • Own the technical direction, architecture, and engineering standards for the MLOps platform.
  • Serve as the primary technical point of contact for Autonomy, Data Platform, and TeleOp on model lifecycle and platform contracts.
  • Design and operate the dataset lifecycle, including versioning, lineage, splits, and labeling handoff.
  • Ensure models can be traced back to the exact data and code used to produce them.
  • Build and operate the model registry, including artifacts, metadata, evaluation results, lineage, and approval workflows.
  • Define the promotion path from trained models to qualified models to deployed robot policies.
  • Create offline benchmarks, simulation rollouts, and policy-gating harnesses for model qualification.
  • Develop metrics and evaluation frameworks that the autonomy team can use to gate releases.
  • Own the packaging, versioning, rollback, and observability path from registered model to inference on Apollo robots.
  • Mentor engineers through code review, design review, and direct collaboration while influencing platform adoption across teams.

Requirements

  • Deep proficiency in Python and at least one systems-level language such as Go, Rust, or C++.
  • Proven experience owning and delivering an MLOps platform end-to-end in a production environment.
  • Experience with dataset versioning tools or equivalents such as DVC, LakeFS, or Delta.
  • Experience with experiment tracking tools such as MLflow, Weights & Biases, or Determined.
  • Strong background designing service-oriented systems on Kubernetes.
  • Experience defining evaluation and qualification frameworks for high-stakes ML systems such as robotics, safety-critical, or customer-facing products.
  • Experience leading technical projects end-to-end, including architecture, implementation, validation, and iteration.
  • Ability to lead by influence across teams and mentor other engineers.
  • Proficiency with cloud infrastructure such as AWS, GCP, or Azure, plus Docker, Git, and modern CI/CD workflows.
  • Master's degree in Computer Science, Machine Learning, or a related technical field preferred; Bachelor's degree considered with exceptional experience.
  • 8+ years of professional software engineering experience in ML platforms or related infrastructure, or 4+ years of direct hands-on experience owning an MLOps platform that shipped models to production.
  • Preferred: Experience deploying ML models to edge or embedded targets such as ONNX Runtime, TensorRT, or robot fleets.
  • Preferred: Experience with RL training and evaluation infrastructure for embodied agents.
  • Preferred: Familiarity with humanoid robotics, dexterous manipulation, or teleoperation data domains.
  • Preferred: Experience with simulation-in-the-loop evaluation using IsaacSim, MuJoCo, or equivalent.
  • Preferred: Familiarity with policy gating, shadow deployments, or staged rollout strategies for autonomy.
  • Preferred: Open-source contributions to MLOps tooling such as MLflow, BentoML, KServe, or Ray Serve.

Interested in this position?

Apply directly on the company website

Apply Now

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